There are existing works that exploit both LoS and NLoS paths for localization, e.g.,  [1]}, [2]}, [3]}. However, in this paper, for the sake of tractability/simplicity, we only focus on extracting the angular parameters of the LoS path for the 3D localization. It will be shown in the results (see Section ) that our approach can achieve high-precision localization performance up to cm level. By following this principle, let us define the matrices \({\mathbf {G}}_{i,1} \triangleq [\frac{\partial \mu _i}{\partial \theta _{i,1}}, \frac{\partial \mu _i}{\partial \phi _{i,1}}] \in \mathbb {C}^{K \times 2}\)  and \({\mathbf {G}}_{i,2} \triangleq [\frac{\partial \mu _i}{\partial d_{i,1}}, \frac{\partial \mu _i}{\partial \theta _{i,2}}, \frac{\partial \mu _i}{\partial \phi _{i,2}}, \frac{\partial \mu _i}{\partial d_{i,2}}, \cdots ,\frac{\partial \mu _i}{\partial \theta _{i,L_i}}, \frac{\partial \mu _i}{\partial \phi _{i,L_i}}, \frac{\partial \mu _i}{\partial d_{i,L_i}} ] \in \mathbb {C}^{K \times (3 L_i -2)}\) , which are the collections of the partial derivatives related to the angular parameters in the LoS path of \({\mathbf {h}}_i\)  and that of the remaining ones, respectively. Therefore, \({\mathbf {J}}^{-1}(\nu _i)\)  in (REF ) can be presented in the form of [4]}, [5]}:
\(&{\mathbf {J}}^{-1}(\nu _i)= \nonumber \\& \frac{\sigma _i^2}{2P}\!\!\begin{bmatrix}\Big ({\mathbf {G}}_{i,1}^\mathsf {H} ({\mathbf {I}}_{K} \!-\! {\mathbf {P}}_{{\mathbf {G}}_{i,2}}){\mathbf {G}}_{i,1}\Big )^{-1} & * \\* & \!\!\!\!\!\!\Big ({\mathbf {G}}_{i,2}^\mathsf {H} ({\mathbf {I}}_{K} \!-\! {\mathbf {P}}_{{\mathbf {G}}_{i,1}}){\mathbf {G}}_{i,2} \Big )^{-1}\end{bmatrix}\!\!,\) 
